some updates

services/anthropic_client.py

@@ -15,15 +15,35 @@ class AnthropicClient:
     def __init__(self, api_key: str = None):
         """Initialize with optional custom API key."""
         self.api_key = api_key or os.getenv("ANTHROPIC_API_KEY")
-        self.client = anthropic.Anthropic(api_key=self.api_key)
-        self.async_client = anthropic.AsyncAnthropic(api_key=self.api_key)
+        self.available = bool(self.api_key)
+        
+        if self.available:
+            self.client = anthropic.Anthropic(api_key=self.api_key)
+            self.async_client = anthropic.AsyncAnthropic(api_key=self.api_key)
+        else:
+            self.client = None
+            self.async_client = None
+            print("⚠️ Anthropic: No API key found - service disabled")
+        
         self.model = "claude-sonnet-4-20250514"
     
+    def is_available(self) -> bool:
+        """Check if the client is available."""
+        return self.available
+    
     async def analyze_emotion(self, user_input: str, system_prompt: str) -> dict:
         """
         Analyze user's emotional state with nuance.
         Returns structured emotion data.
         """
+        if not self.available or not self.async_client:
+            return {
+                "primary_emotions": ["neutral"],
+                "intensity": 5,
+                "pip_expression": "neutral",
+                "intervention_needed": False
+            }
+        
         response = await self.async_client.messages.create(
             model=self.model,
             max_tokens=1024,
@@ -58,6 +78,13 @@ class AnthropicClient:
         """
         Decide what action Pip should take based on emotional state.
         """
+        if not self.available or not self.async_client:
+            return {
+                "action": "reflect",
+                "image_style": "gentle",
+                "voice_tone": "warm"
+            }
+        
         response = await self.async_client.messages.create(
             model=self.model,
             max_tokens=512,
@@ -93,6 +120,10 @@ class AnthropicClient:
         """
         Generate Pip's conversational response with streaming.
         """
+        if not self.available or not self.async_client:
+            yield "I'm here with you. Let me think about what you shared..."
+            return
+        
         messages = conversation_history or []
         
         # Add context about current emotional state
@@ -126,6 +157,10 @@ Voice tone: {action.get('voice_tone', 'warm')}
         """
         Generate a gentle intervention response for concerning emotional states.
         """
+        if not self.available or not self.async_client:
+            yield "I hear you, and I want you to know that what you're feeling matters. Take a moment to breathe..."
+            return
+        
         context = f"""
 [INTERVENTION NEEDED]
 User message: {user_input}
@@ -151,6 +186,9 @@ Do NOT be preachy or clinical.
         Generate text response for a given prompt.
         Used for summaries and other text generation needs.
         """
+        if not self.available or not self.async_client:
+            return ""
+        
         try:
             response = await self.async_client.messages.create(
                 model=self.model,

services/elevenlabs_client.py

@@ -54,13 +54,23 @@ class ElevenLabsClient:
     }
     
     def __init__(self):
-        self.client = AsyncElevenLabs(
-            api_key=os.getenv("ELEVENLABS_API_KEY")
-        )
+        api_key = os.getenv("ELEVENLABS_API_KEY")
+        self.available = bool(api_key)
+        
+        if self.available:
+            self.client = AsyncElevenLabs(api_key=api_key)
+        else:
+            self.client = None
+            print("⚠️ ElevenLabs: No API key found - voice disabled")
+        
         # Default voice - can be customized or created via Voice Design
         self.default_voice_id = "21m00Tcm4TlvDq8ikWAM"  # Rachel - warm, friendly
         self.pip_voice_id = None  # Will be set if custom voice is created
     
+    def is_available(self) -> bool:
+        """Check if the client is available."""
+        return self.available
+    
     async def speak(
         self, 
         text: str, 
@@ -71,6 +81,9 @@ class ElevenLabsClient:
         Generate speech from text with emotional tone matching.
         Returns audio bytes (mp3).
         """
+        if not self.available or not self.client:
+            return None
+        
         try:
             model = self.MODELS["flash"] if use_fast_model else self.MODELS["expressive"]
             voice_settings = self.TONE_SETTINGS.get(tone, self.TONE_SETTINGS["warm"])
@@ -106,6 +119,9 @@ class ElevenLabsClient:
         Stream audio generation for lower latency.
         Yields audio chunks as they're generated.
         """
+        if not self.available or not self.client:
+            return
+        
         try:
             model = self.MODELS["flash"]
             voice_settings = self.TONE_SETTINGS.get(tone, self.TONE_SETTINGS["warm"])
@@ -130,6 +146,9 @@ class ElevenLabsClient:
         """
         Get list of available voices.
         """
+        if not self.available or not self.client:
+            return []
+        
         try:
             voices = await self.client.voices.get_all()
             return [{"id": v.voice_id, "name": v.name} for v in voices.voices]

services/gemini_client.py

@@ -27,14 +27,22 @@ class GeminiClient:
     def __init__(self, api_key: str = None):
         """Initialize with optional custom API key."""
         self.api_key = api_key or os.getenv("GOOGLE_API_KEY")
-        if self.api_key:
+        self.available = bool(self.api_key)
+        
+        if self.available:
             genai.configure(api_key=self.api_key)
+        else:
+            print("⚠️ Gemini: No API key found - service disabled")
         
         # Model instances (lazy loaded)
         self._fast_model = None
         self._pro_model = None
         self._image_model = None
     
+    def is_available(self) -> bool:
+        """Check if the client is available."""
+        return self.available
+    
     def _get_fast_model(self):
         """Get fast model for quick responses."""
         if self._fast_model is None:
@@ -62,6 +70,14 @@ class GeminiClient:
         Analyze emotional content of user input.
         Returns structured emotion analysis.
         """
+        if not self.available:
+            return {
+                "primary_emotions": ["neutral"],
+                "intensity": 5,
+                "pip_expression": "neutral",
+                "intervention_needed": False
+            }
+        
         try:
             model = self._get_pro_model()
             
@@ -149,6 +165,9 @@ Respond with ONLY valid JSON, no markdown."""
         Generate a quick acknowledgment (< 500ms target).
         Uses the fastest available model.
         """
+        if not self.available:
+            return "I hear you..."
+        
         try:
             model = self._get_fast_model()
             
@@ -269,6 +288,9 @@ Respond with care, warmth, and appropriate resources if needed."""
         """
         Generate text (for prompts, summaries, etc).
         """
+        if not self.available:
+            return None
+        
         try:
             model = self._get_pro_model()
             response = await model.generate_content_async(
@@ -323,6 +345,9 @@ Generate the enhanced image prompt only, no explanation."""
         Generate an image using Gemini's image generation model.
         Returns base64 encoded image.
         """
+        if not self.available:
+            return None
+        
         try:
             model = self._get_image_model()
             

services/modal_flux.py

@@ -34,6 +34,14 @@ class ModalFluxClient:
     def __init__(self):
         self.hf_token = os.getenv("HF_TOKEN")
         self.modal_endpoint = os.getenv("MODAL_FLUX_ENDPOINT")  # If deployed
+        self.available = bool(self.hf_token) or bool(self.modal_endpoint)
+        
+        if not self.available:
+            print("⚠️ HuggingFace/Modal: No tokens found - image generation limited")
+    
+    def is_available(self) -> bool:
+        """Check if the client is available."""
+        return self.available
     
     async def generate_image(
         self, 

services/openai_client.py

@@ -23,6 +23,9 @@ class OpenAIClient:
         Generate an image using GPT-4o / DALL-E 3.
         Returns base64 encoded image or URL.
         """
+        if not self.available or not self.client:
+            return None
+        
         try:
             response = await self.client.images.generate(
                 model="dall-e-3",
@@ -42,6 +45,9 @@ class OpenAIClient:
         """
         Transcribe audio using Whisper.
         """
+        if not self.available or not self.client:
+            return ""
+        
         try:
             with open(audio_file_path, "rb") as audio_file:
                 response = await self.client.audio.transcriptions.create(
@@ -58,6 +64,9 @@ class OpenAIClient:
         """
         Transcribe audio from bytes using Whisper.
         """
+        if not self.available or not self.client:
+            return ""
+        
         try:
             # Create a file-like object from bytes
             response = await self.client.audio.transcriptions.create(

services/sambanova_client.py

@@ -14,10 +14,18 @@ class SambanovaClient:
     """SambaNova-powered fast inference for Pip."""
     
     def __init__(self):
-        self.client = AsyncOpenAI(
-            api_key=os.getenv("SAMBANOVA_API_KEY"),
-            base_url=os.getenv("SAMBANOVA_BASE_URL", "https://api.sambanova.ai/v1")
-        )
+        api_key = os.getenv("SAMBANOVA_API_KEY")
+        self.available = bool(api_key)
+        
+        if self.available:
+            self.client = AsyncOpenAI(
+                api_key=api_key,
+                base_url=os.getenv("SAMBANOVA_BASE_URL", "https://api.sambanova.ai/v1")
+            )
+        else:
+            self.client = None
+            print("⚠️ SambaNova: No API key found - service disabled")
+        
         # Using Llama 3.1 or DeepSeek on SambaNova
         self.model = "Meta-Llama-3.1-8B-Instruct"
         self._rate_limited = False
@@ -43,7 +51,9 @@ class SambanovaClient:
         Generate a quick acknowledgment while heavier processing happens.
         This should be FAST - just a brief "I hear you" type response.
         """
-        # If rate limited, return a fallback
+        # If not available or rate limited, return a fallback
+        if not self.available or not self.client:
+            return "I hear you..."
         if await self._check_rate_limit():
             return "I hear you..."
         
@@ -75,10 +85,14 @@ class SambanovaClient:
         Transform user context into a detailed, vivid image prompt.
         This is where user-specific imagery is crafted.
         """
-        # If rate limited, return a simple prompt
+        emotions = emotion_state.get('primary_emotions', ['peaceful'])
+        fallback = f"A beautiful, calming scene representing {emotions[0] if emotions else 'peace'}, soft colors, dreamy atmosphere"
+        
+        # If not available or rate limited, return a simple prompt
+        if not self.available or not self.client:
+            return fallback
         if await self._check_rate_limit():
-            emotions = emotion_state.get('primary_emotions', ['peaceful'])
-            return f"A beautiful, calming scene representing {emotions[0] if emotions else 'peace'}, soft colors, dreamy atmosphere"
+            return fallback
         
         context = f"""
 User said: "{user_input}"
@@ -119,7 +133,10 @@ Generate a vivid, specific image prompt based on THIS user's context.
         Generate conversational response with streaming.
         Used for load-balanced conversation when Claude is busy.
         """
-        # If rate limited, yield a fallback
+        # If not available or rate limited, yield a fallback
+        if not self.available or not self.client:
+            yield "I understand how you're feeling. Let me take a moment to think about this..."
+            return
         if await self._check_rate_limit():
             yield "I understand how you're feeling. Let me take a moment to think about this..."
             return
@@ -159,14 +176,18 @@ User said: {user_input}
         """
         import json
         
-        # If rate limited, return basic analysis
+        default_response = {
+            "primary_emotions": ["neutral"],
+            "intensity": 5,
+            "pip_expression": "neutral",
+            "intervention_needed": False
+        }
+        
+        # If not available or rate limited, return basic analysis
+        if not self.available or not self.client:
+            return default_response
         if await self._check_rate_limit():
-            return {
-                "primary_emotions": ["neutral"],
-                "intensity": 5,
-                "pip_expression": "neutral",
-                "intervention_needed": False
-            }
+            return default_response
         
         try:
             response = await self.client.chat.completions.create(